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Mathematical Analysis of “Phase Ramping” for Super-Resolution Magnetic Resonance Imaging

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Image Analysis and Recognition (ICIAR 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4141))

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Abstract

Super-resolution image processing algorithms are based on the principle that repeated imaging together with information about the acquisition process may be used to enhance spatial resolution. In the usual implementation, a series of low-resolution images shifted by typically subpixel distances are acquired. The pixels of these low-resolution images are then interleaved and modeled as a blurred image of higher resolution and the same field-of-view. A high-resolution image is then obtained using a standard deconvolution algorithm. Although this approach has been applied in magnetic resonance imaging (MRI), some controversy has surfaced regarding the validity and circumstances under which super-resolution may be applicable. We investigate the factors that limit the applicability of super-resolution MRI.

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Authors and Affiliations

  1. Department of Applied Mathematics, Faculty of Mathematics, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada

    Gregory S. Mayer & Edward R. Vrscay

Authors
  1. Gregory S. Mayer
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  2. Edward R. Vrscay
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Editors and Affiliations

  1. Departamento de Engenharia Electrotécnica e de Computadores da, Faculdade de Engenharia da, Universidade do Porto, Campus FEUP, 4200-465, Porto, Portugal

    Aurélio Campilho

  2. Electrical and Computer Engineering Department, University of Waterloo,  

    Mohamed S. Kamel

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© 2006 Springer-Verlag Berlin Heidelberg

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Mayer, G.S., Vrscay, E.R. (2006). Mathematical Analysis of “Phase Ramping” for Super-Resolution Magnetic Resonance Imaging. In: Campilho, A., Kamel, M.S. (eds) Image Analysis and Recognition. ICIAR 2006. Lecture Notes in Computer Science, vol 4141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11867586_8

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  • DOI: https://doi.org/10.1007/11867586_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44891-4

  • Online ISBN: 978-3-540-44893-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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